
"""simple docstring"""



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a       :						str       		=		logging.get_logger(__name__)

a       :						List[Any]       		=		'''▁'''

a       :						Tuple       		=		{'''vocab_file''': '''sentencepiece.bpe.model'''}

a       :						Tuple       		=		{
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

a       :						int       		=		{
    '''facebook/xglm-564M''': 2_0_4_8,
}




class 		a_    (			_UpperCAmelCase						):
    a   :					Tuple			     =					VOCAB_FILES_NAMES
    a   :					Dict			     =					PRETRAINED_VOCAB_FILES_MAP
    a   :					List[Any]			     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a   :					List[str]			     =					['input_ids', 'attention_mask']







    def __init__(      self      :							Union[str, Any]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							str="<s>"  ,       __UpperCamelCase      :							Dict="</s>"  ,       __UpperCamelCase      :							Optional[Any]="</s>"  ,       __UpperCamelCase      :							Tuple="<s>"  ,       __UpperCamelCase      :							Optional[int]="<unk>"  ,       __UpperCamelCase      :							Union[str, Any]="<pad>"  ,       __UpperCamelCase      :							Optional[Dict[str, Any]] = None  ,       **__UpperCamelCase      :							Optional[int]  ,       )		->None:






        '''simple docstring'''






        _UpperCAmelCase        = {} if sp_model_kwargs is None else sp_model_kwargs

        # Compatibility with the original tokenizer
        _UpperCAmelCase        = 7
        _UpperCAmelCase        = [f"""<madeupword{i}>""" for i in range(self.num_madeup_words  )]

        _UpperCAmelCase        = kwargs.get("""additional_special_tokens"""  ,       []  )
        kwargs["additional_special_tokens"] += [
            word for word in madeup_words if word not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            bos_token=__UpperCamelCase  ,       eos_token=__UpperCamelCase  ,       unk_token=__UpperCamelCase  ,       sep_token=__UpperCamelCase  ,       cls_token=__UpperCamelCase  ,       pad_token=__UpperCamelCase  ,       sp_model_kwargs=self.sp_model_kwargs  ,       **__UpperCamelCase  ,       )

        _UpperCAmelCase        = spm.SentencePieceProcessor(**self.sp_model_kwargs  )
        self.sp_model.Load(str(__UpperCamelCase  )  )
        _UpperCAmelCase        = vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
        _UpperCAmelCase        = 1

        # Mimic fairseq token-to-id alignment for the first 4 token
        _UpperCAmelCase        = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

        _UpperCAmelCase        = len(self.sp_model  )
        _UpperCAmelCase        = {f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words  )}
        self.fairseq_tokens_to_ids.update(__UpperCamelCase  )

        _UpperCAmelCase        = {v: k for k, v in self.fairseq_tokens_to_ids.items()}







    def __getstate__(      self      :							Tuple  )		->str:






        '''simple docstring'''






        _UpperCAmelCase        = self.__dict__.copy()
        _UpperCAmelCase        = None
        _UpperCAmelCase        = self.sp_model.serialized_model_proto()
        return state







    def __setstate__(      self      :							List[Any]  ,       __UpperCamelCase      :							List[Any]  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = d

        # for backward compatibility
        if not hasattr(self  ,       """sp_model_kwargs"""  ):
            _UpperCAmelCase        = {}

        _UpperCAmelCase        = spm.SentencePieceProcessor(**self.sp_model_kwargs  )
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )







    def   _snake_case       (      self      :							str  ,       __UpperCamelCase      :							List[int]  ,       __UpperCamelCase      :							Optional[List[int]] = None  )		->List[int]:






        '''simple docstring'''






        if token_ids_a is None:
            return [self.sep_token_id] + token_ids_a
        _UpperCAmelCase        = [self.sep_token_id]
        return sep + token_ids_a + sep + sep + token_ids_a







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							List[int]  ,       __UpperCamelCase      :							Optional[List[int]] = None  ,       __UpperCamelCase      :							bool = False  )		->List[int]:






        '''simple docstring'''






        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=__UpperCamelCase  ,       token_ids_a=__UpperCamelCase  ,       already_has_special_tokens=__UpperCamelCase  )

        if token_ids_a is None:
            return [1] + ([0] * len(__UpperCamelCase  ))
        return [1] + ([0] * len(__UpperCamelCase  )) + [1, 1] + ([0] * len(__UpperCamelCase  ))







    def   _snake_case       (      self      :							Union[str, Any]  ,       __UpperCamelCase      :							List[int]  ,       __UpperCamelCase      :							Optional[List[int]] = None  )		->List[int]:






        '''simple docstring'''






        _UpperCAmelCase        = [self.sep_token_id]

        if token_ids_a is None:
            return len(sep + token_ids_a  ) * [0]
        return len(sep + token_ids_a + sep + sep + token_ids_a  ) * [0]







    @property
    def   _snake_case       (      self      :							Any  )		->Any:






        '''simple docstring'''






        return len(self.sp_model  ) + self.fairseq_offset + self.num_madeup_words







    def   _snake_case       (      self      :							Optional[Any]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = {self.convert_ids_to_tokens(__UpperCamelCase  ): i for i in range(self.vocab_size  )}
        vocab.update(self.added_tokens_encoder  )
        return vocab







    def   _snake_case       (      self      :							Optional[Any]  ,       __UpperCamelCase      :							str  )		->List[str]:






        '''simple docstring'''






        return self.sp_model.encode(__UpperCamelCase  ,       out_type=__UpperCamelCase  )







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							Tuple  )		->Optional[int]:






        '''simple docstring'''






        if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
        _UpperCAmelCase        = self.sp_model.PieceToId(__UpperCamelCase  )

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							Tuple  )		->Union[str, Any]:






        '''simple docstring'''






        if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset  )







    def   _snake_case       (      self      :							Union[str, Any]  ,       __UpperCamelCase      :							Any  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = """""".join(__UpperCamelCase  ).replace(__UpperCamelCase  ,       """ """  ).strip()
        return out_string







    def   _snake_case       (      self      :							Union[str, Any]  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Optional[str] = None  )		->Tuple[str]:






        '''simple docstring'''






        if not os.path.isdir(__UpperCamelCase  ):
            logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
            return
        _UpperCAmelCase        = os.path.join(
            __UpperCamelCase  ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

        if os.path.abspath(self.vocab_file  ) != os.path.abspath(__UpperCamelCase  ) and os.path.isfile(self.vocab_file  ):
            copyfile(self.vocab_file  ,       __UpperCamelCase  )
        elif not os.path.isfile(self.vocab_file  ):
            with open(__UpperCamelCase  ,       """wb"""  ) as fi:
                _UpperCAmelCase        = self.sp_model.serialized_model_proto()
                fi.write(__UpperCamelCase  )

        return (out_vocab_file,)

"""simple docstring"""



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a       :						Any       		=		logging.get_logger(__name__)


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = RobertaPreLayerNormConfig.from_pretrained(
        _A       ,   architectures=["""RobertaPreLayerNormForMaskedLM"""]   )

    # convert state_dict
    _UpperCAmelCase        = torch.load(hf_hub_download(repo_id=_A       ,   filename="""pytorch_model.bin"""   )   )
    _UpperCAmelCase        = {}
    for tensor_key, tensor_value in original_state_dict.items():
        # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
        if tensor_key.startswith("""roberta."""   ):
            _UpperCAmelCase        = """roberta_prelayernorm.""" + tensor_key[len("""roberta."""   ) :]

        # The original implementation contains weights which are not used, remove them from the state_dict
        if tensor_key.endswith(""".self.LayerNorm.weight"""   ) or tensor_key.endswith(""".self.LayerNorm.bias"""   ):
            continue

        _UpperCAmelCase        = tensor_value

    _UpperCAmelCase        = RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=_A       ,   config=_A       ,   state_dict=_A   )
    model.save_pretrained(_A   )

    # convert tokenizer
    _UpperCAmelCase        = AutoTokenizer.from_pretrained(_A   )
    tokenizer.save_pretrained(_A   )


if __name__ == "__main__":
 a       :						Tuple       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint-repo''',
     default=None,
     type=str,
     required=True,
     help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a       :						Union[str, Any]       		=		parser.parse_args()
 convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)